Search Results for author: Qingfeng Sun

Found 13 papers, 7 papers with code

WarriorCoder: Learning from Expert Battles to Augment Code Large Language Models

no code implementations23 Dec 2024 Huawen Feng, Pu Zhao, Qingfeng Sun, Can Xu, Fangkai Yang, Lu Wang, Qianli Ma, QIngwei Lin, Saravan Rajmohan, Dongmei Zhang, Qi Zhang

Despite recent progress achieved by code large language models (LLMs), their remarkable abilities are largely dependent on fine-tuning on the high-quality data, posing challenges for data collection and annotation.

Data Augmentation Diversity

Towards a Unified Paradigm: Integrating Recommendation Systems as a New Language in Large Models

no code implementations22 Dec 2024 Kai Zheng, Qingfeng Sun, Can Xu, Peng Yu, Qingwei Guo

We introduce a new concept, "Integrating Recommendation Systems as a New Language in Large Models" (RSLLM), which combines the strengths of traditional recommenders and LLMs.

Language Modeling Language Modelling +1

AgentGen: Enhancing Planning Abilities for Large Language Model based Agent via Environment and Task Generation

1 code implementation1 Aug 2024 Mengkang Hu, Pu Zhao, Can Xu, Qingfeng Sun, JianGuang Lou, QIngwei Lin, Ping Luo, Saravan Rajmohan

Moreover, to increase the difficulty diversity of generated planning tasks, we propose a bidirectional evolution method, Bi-Evol, that evolves planning tasks from easier and harder directions to synthesize a task set with a smoother difficulty curve.

Diversity Language Modeling +2

Arena Learning: Build Data Flywheel for LLMs Post-training via Simulated Chatbot Arena

no code implementations15 Jul 2024 Haipeng Luo, Qingfeng Sun, Can Xu, Pu Zhao, QIngwei Lin, JianGuang Lou, Shifeng Chen, Yansong Tang, Weizhu Chen

In this paper, we introduce Arena Learning, an innovative offline strategy designed to simulate these arena battles using AI-driven annotations to evaluate battle outcomes, thus facilitating the continuous improvement of the target model through both supervised fine-tuning and reinforcement learning.

Chatbot

WizardMath: Empowering Mathematical Reasoning for Large Language Models via Reinforced Evol-Instruct

1 code implementation18 Aug 2023 Haipeng Luo, Qingfeng Sun, Can Xu, Pu Zhao, JianGuang Lou, Chongyang Tao, Xiubo Geng, QIngwei Lin, Shifeng Chen, Yansong Tang, Dongmei Zhang

Large language models (LLMs), such as GPT-4, have shown remarkable performance in natural language processing (NLP) tasks, including challenging mathematical reasoning.

Ranked #52 on Arithmetic Reasoning on GSM8K (using extra training data)

Arithmetic Reasoning GSM8K +2

WizardCoder: Empowering Code Large Language Models with Evol-Instruct

3 code implementations14 Jun 2023 Ziyang Luo, Can Xu, Pu Zhao, Qingfeng Sun, Xiubo Geng, Wenxiang Hu, Chongyang Tao, Jing Ma, QIngwei Lin, Daxin Jiang

Moreover, our model even outperforms the largest closed LLMs, Anthropic's Claude and Google's Bard, on HumanEval and HumanEval+.

Code Generation HumanEval

Self-Supervised Multi-Modal Sequential Recommendation

1 code implementation26 Apr 2023 Kunzhe Song, Qingfeng Sun, Can Xu, Kai Zheng, Yaming Yang

To address this issue, we propose a dual-tower retrieval architecture for sequence recommendation.

Contrastive Learning Retrieval +1

WizardLM: Empowering Large Language Models to Follow Complex Instructions

4 code implementations24 Apr 2023 Can Xu, Qingfeng Sun, Kai Zheng, Xiubo Geng, Pu Zhao, Jiazhan Feng, Chongyang Tao, Daxin Jiang

In this paper, we show an avenue for creating large amounts of instruction data with varying levels of complexity using LLM instead of humans.

Instruction Following

Multimodal Dialogue Response Generation

no code implementations ACL 2022 Qingfeng Sun, Yujing Wang, Can Xu, Kai Zheng, Yaming Yang, Huang Hu, Fei Xu, Jessica Zhang, Xiubo Geng, Daxin Jiang

In such a low-resource setting, we devise a novel conversational agent, Divter, in order to isolate parameters that depend on multimodal dialogues from the entire generation model.

Dialogue Generation Response Generation +1

Hierarchical Attention Prototypical Networks for Few-Shot Text Classification

no code implementations IJCNLP 2019 Shengli Sun, Qingfeng Sun, Kevin Zhou, Tengchao Lv

Most of the current effective methods for text classification tasks are based on large-scale labeled data and a great number of parameters, but when the supervised training data are few and difficult to be collected, these models are not available.

Few-Shot Text Classification General Classification +1

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